In the fast-paced world of AI, the quality of your data can make or break model performance. That’s why SuperAnnotate and Amazon Web Services (AWS) have partnered to streamline how you build training datasets and evaluate models.
SuperAnnotate joins the AWS ISV Accelerate Program
Joining the AWS ISV Accelerate Program is a significant milestone for SuperAnnotate. It underscores our commitment to meeting the highest quality, security, and customer success standards. Acceptance into this co-sell program required a comprehensive review of our architecture and security standards and proof of our ability to deliver success to joint SuperAnnotate and AWS customers across multiple industry verticals.
Simplified procurement
SuperAnnotate is available for purchase on the AWS Marketplace. Customers who purchase via this method will see their SuperAnnotate license on their AWS bill. This allows customers to consolidate vendors and spending, potentially unlocking greater incentives with AWS.
AWS integrations for end-to-end AI pipelines
Data preparation accounts for up to 80% of the time in an AI project, especially when cumbersome infrastructure hinders data transfer. To help customers move from data to results faster, SuperAnnotate integrates with multiple AWS services, from data storage to model training and evaluation:
- AWS S3: Our platform’s integration with S3 makes data storage, transfer, and accessibility easy and secure.
- Amazon SageMaker: SuperAnnotate integrates directly with SageMaker, allowing you to manage and prepare data for model fine-tuning within the SageMaker environment. This results in faster iterations and more accurate models.
- Amazon Bedrock: SuperAnnotate powers foundational model customization on Bedrock, ensuring high-quality, annotated datasets for large-scale AI applications.
These integrations reduce time and resource costs, allowing you to build higher-quality models and bring them to production faster.
Fine-tuning foundation models: Insights from our recent webinar
In a recent webinar with AWS, we explored practical strategies for fine-tuning foundation models on AWS Bedrock using SuperAnnotate. Attendees learned how to improving model performance with high-quality data and practical examples of SuperAnnotate’s capabilities. Watch the full webinar here to see how our solutions can accelerate your AI initiatives.
Visit us at AWS re:Invent
Meet the SuperAnnotate team at AWS re:Invent. We’re located at booth #217 in the AWS Marketplace Partner Pavilion. Stop by to see our tools in action and discuss how we can help you advance your AI projects.
Use cases covered by SuperAnnotate and AWS
SuperAnnotate and AWS empower enterprises to build and deploy advanced AI models efficiently across a variety of applications:
- Training foundation models: After learning foundational language skills and knowledge on vast pre-training datasets, foundation models are adapted to use cases and human preferences in another data-intensive process called post-training. Leading foundation models require high complexity and high-quality datasets, which require excellent quality control tooling, workflow orchestration, management tools, and more - which SuperAnnotate provides.
- Enhancing RAG systems: Retrieval-augmented generation (RAG) is becoming an essential application for language models in enterprise settings. However, many organizations face challenges in achieving the expected performance. SuperAnnotate provides companies with evaluation data to make informed choices on model selection, prompt strategies, and training data to fine-tune embedding models for specific domains, improving RAG outcomes.
- Evaluating agentic systems: Interest in agentic systems, where foundation models get access to tools and are set up to perform tasks autonomously, is growing among our enterprise clients. However, evaluating these systems is complex, as many reasoning steps remain hidden from the user. SuperAnnotate enables evaluators to visualize each step in the reasoning process, providing insights beyond just final output ratings.
- Model evaluations: While public benchmarks on general datasets provide a baseline, they offer limited insight into how models perform on proprietary, domain-specific data in enterprise environments. SuperAnnotate empowers companies to conduct thorough evaluations using third-party evaluators or internal domain experts, ensuring reliable performance before deploying models in customer-facing or internal applications.
- Synthetic data generation: As AI performance advances, model-generated synthetic data is increasingly used to enrich human-curated training datasets. However, effectively integrating synthetic data requires care, especially in more complex domains. SuperAnnotate’s platform enables companies to establish hybrid data pipelines, blending synthetic and human-labeled data for optimal results.
With SuperAnnotate and AWS, enterprises can accelerate the AI lifecycle—from data preparation to evaluation—and turn high-quality datasets into powerful, reliable AI models faster.
Ready to learn more? Contact us to explore how SuperAnnotate and AWS can help you advance your AI initiatives.
